




"""simple docstring"""

import baseaa






def 		_snake_case	(   _snake_case				:   str				)       ->   bytes:






      '''simple docstring'''


      return baseaa.aaaencode(string.encode('utf-8'				)				)






def 		_snake_case	(   _snake_case				:   bytes				)       ->   str:






      '''simple docstring'''


      return baseaa.aaadecode(_snake_case				).decode('utf-8'				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
